Change In Arid Regions Research Articles

Spatiotemporal Changes in Vegetation Cover and Soil Moisture in the Lower Reaches of the Heihe River Under Climate Change

As global climate change intensifies, arid land ecosystems face increasing challenges. Vegetation, a key indicator of climate variation, is highly responsive to meteorological factors such as temperature (Tem), precipitation (Pre), and soil moisture (SM). Understanding how fractional vegetation cover (FVC) responds to climate change in arid regions is critical for mitigating its impacts. This study utilizes MOD13Q1-NDVI data from 2000 to 2022, alongside corresponding Tem, Pre, and SM data, to explore the dynamics and underlying mechanisms of SM and FVC in the context of climate change. The results reveal that both climate change and human activities exacerbate vegetation degradation, underscoring its vulnerability. A strong correlation between FVC and both Tem and Pre suggests that these factors significantly influence FVC variability. In conclusion, FVC in the lower reaches of the Heihe River is shaped by a complex interplay of Tem, Pre, SM, and human activities. The findings provide a scientific basis and decision-making support for ecological conservation and water resource management in the lower reaches of the Heihe River, aiding in the development of more effective strategies to address future climate challenges.

Predicting the impact of climate change on crop water footprint using CMIP6 in the Shule River Basin, China

Quantitatively predicting the impacts of climate change on water demands of various crops is essential for developing measures to ensure food security, sustainable agriculture, and water resources management, especially in arid regions. This study explored the water footprints (WFs) of nine major crops in the middle and downstream areas of Shule River Basin, Northwest China, from 1989 to 2020 using the WF theory and CROPWAT model and predicted the future WFs of these crops under four emission and socio-economic pathway (SSPs-RCPs) scenarios, which provides scientific support for actively responding to the negative impacts of climate change in arid regions. Results indicated: (1) an increasing trend of the overall crop WF, with blue WF accounting for 80.31–99.33% of the total WF in the last 30 years. Owing to differences of planting structure, water-conservation technologies, and other factors, the multi-year average WF per unit area of crops was 0.75 × 104 m3 hm−2 in downstream area, which was higher than that in midstream area (0.57 × 104 m3 hm−2) in the last 30 years; therefore agricultural water use efficiency in the downstream area was lower than that in the midstream area, implying that the midstream area has more efficient agricultural water utilization. (2) an initial increase and then decrease of crop WFs in the study area under SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios by the end of the century, reaching their peak in 2030s which was higher than that from 1989 to 2020; with the maximum growth rates in the midstream area ranging from −0.85% in SSP5-8.5 to 5.33% in SSP2-4.5 and 29.74% in SSP5-8.5 to 34.71% in SSP2-4.5 in the downstream area. The local agricultural water demand would continue to increase and water scarcity issues would be more severe in the next 10–20 years, affecting downstream areas more. Under the SSP3-7.0 scenario, crop WF values of the midstream and downstream regions will be 2.63 × 108 m3 and 4.22 × 108 m3 in 2030, respectively, which is significantly higher than those of other scenarios and show a long-term growth trend. The growth rate of the midstream and downstream regions will reach 44.71% and 81.12%, respectively, by the end of this century, so the local agricultural water use would be facing more strain if this scenario materializes in the future. Therefore, the Shule River Basin should encourage development of water-saving irrigation technologies, adjust the planting ratio of high water consuming crops, and identify other measures to improve water resource utilization efficiency to cope with future water resource pressures.

Dynamic simulation and projection of ESV changes in arid regions caused by urban growth under climate change scenarios.

Spatial simulation and projection of ecosystem services value (ESV) changes caused by urban growth are important for sustainable development in arid regions. We developed a new model of cellular automata based grasshopper optimization algorithm (named GOA-CA) for simulating urban growth patterns and assessing the impacts of urban growth on ESV changes under climate change scenarios. The results show that GOA-CA yielded overall accuracy exceeding 98%, and FOM for 2010 and 2020 were 43.2% and 38.1%, respectively, indicating the effectiveness of the model. The prairie lost the highest economic ESVs (192 million USD) and the coniferous yielded the largest economic ESV increase (292 million USD) during 2000-2020. Using climate change scenarios as urban future land use demands, we projected three scenarios of the urban growth of Urumqi for 2050 and their impacts on ESV. Our model can be easily applied to simulating urban development, analyzing its impact on ESV and projecting future scenarios in global arid regions.

Feeding Management of Small Ruminants as A Strategic Tool to Mitigate The Negative Impact of Climate Changes in Arid Regions of Egypt: A Case Study of Assaf Sheep

Feeding Management of Small Ruminants as A Strategic Tool to Mitigate The Negative Impact of Climate Changes in Arid Regions of Egypt: A Case Study of Assaf Sheep

Urbanization Effect on Changes in Extreme Climate Events in Urumqi, China, from 1976 to 2018

This study investigates, for the first time, the urbanization effect (UE) on local extreme climate events in Urumqi, China, based on 22 indices of climate extremes, which are calculated with daily observation data from 1976 to 2018. These analyses reveal a pronounced nocturnal urban heat island (UHI) effect and a daytime urban cold island (UCI) effect. Due to Urumqi’s arid climate background, the UCI effect is considered a unique feature of the UE, which significantly differs from those in eastern and northern China. The UE on the TR20 index (number of days with minimum daily temperature exceeding 20 °C) reached 5.22 d/10a, indicating that urbanization has led to a fast increase in the number of hot nights in Urumqi. The absolute averaged UE on the indices measuring the frequency of warm events is about twice as large as that on the indices measuring the frequency of cold events, while that on the indices measuring the intensity of warm events is about one third of that on the indices measuring the intensity of cold events. The highest averaged urbanization contributions (UCs) to the extreme warm and cold events are represented by the frequency indices and the intensity indices, respectively, while those contributing to the extreme precipitation events are represented by the duration indices. Moreover, urbanization probably exacerbates the degree of wetting in the overall “warming and wetting” climate trend of the region. These findings can be seen as new evidence to provide scientific basis for further investigation of the UE on climate changes in arid regions.

Plant macrofossil data for 48-0 ka in the USGS North American Packrat Midden Database, version 5.0

Plant macrofossils from packrat (Neotoma spp.) middens provide direct evidence of past vegetation changes in arid regions of North America. Here we describe the newest version (version 5.0) of the U.S. Geological Survey (USGS) North American Packrat Midden Database. The database contains published and contributed data from 3,331 midden samples collected in southwest Canada, the western United States, and northern Mexico, with samples ranging in age from 48 ka to the present. The database includes original midden-sample macrofossil counts and relative-abundance data along with a standardized relative-abundance scheme that makes it easier to compare macrofossil data across midden-sample sites. In addition to the midden-sample data, this version of the midden database includes calibrated radiocarbon (14C) ages for the midden samples and plant functional type (PFT) assignments for the midden taxa. We also provide World Wildlife Fund ecoregion assignments and climate and bioclimate data for each midden-sample site location. The data are provided in tabular (.xlsx), comma-separated values (.csv), and relational database (.mdb) files.

Explaining the role of land use changes on land surface temperature in an arid and semi-arid metropolitan area with multi-scale spatial regression analysis.

Urban Heat Islands (UHIs), Land Surface Temperature (LST), and Land Use Land Cover (LULC) changes are critical environmental concerns that require continuous monitoring and assessment, especially in cities within arid and semi-arid (ASA) climates. Despite the abundance of research in tropical, Mediterranean, and cold climates, there is a significant knowledge gap for cities in the Middle East with ASA climates. This study aimed to examine the effects of LULC change, population, and wind speed on LST in the Mashhad Metropolis, a city with an ASA climate, over a 30-year period. The research underscores the importance of environmental monitoring and assessment in understanding and mitigating the impacts of urbanization and climate change. Our research combines spatial regression models, multi-scale and fine-scale analyses, seasonal and city outskirts considerations, and long-term change assessments. We used Landsat satellite imagery, a crucial tool for environmental monitoring, to identify LULC changes and their impact on LST at three scales. The relationships were analyzed using Ordinary Least Squares (OLS) and Spatial Error Model (SEM) regressions, demonstrating the value of these techniques in environmental assessment. Our findings highlight the role of environmental factors in shaping LST. A decrease in vegetation and instability of water bodies significantly increased LST over the study period. Bare lands and rocky terrains had the most substantial effect on LST. At the same time, built-up areas resulted in Urban Cooling Islands (UCIs) due to their lower temperatures compared to surrounding bare lands. The Normalized Difference Vegetation Index (NDVI) and Dry Bare-Soil Index (DBSI) were the most effective indices impacting LST in ASA regions, and the 30×30 m2 micro-scale provides more precise results in regression models, underscoring their importance in environmental monitoring. Our study provided a comprehensive understanding of the relationship between LULC changes and LST in an ASA environment, contributing significantly to the literature on environmental change in arid regions and the methodologies for monitoring such changes. Future research should aim to validate and expand additional LST-affecting factors and test our approach and findings in other ASA regions, considering the unique characteristics of these areas and the importance of tailored environmental monitoring and assessment approaches.

Detecting, Analyzing, and Predicting Land Use/Land Cover (LULC) Changes in Arid Regions Using Landsat Images, CA-Markov Hybrid Model, and GIS Techniques

Understanding the change dynamics of land use and land cover (LULC) is critical for efficient ecological management modification and sustainable land-use planning. This work aimed to identify, simulate, and predict historical and future LULC changes in the Sohag Governorate, Egypt, as an arid region. In the present study, the detection of historical LULC change dynamics for time series 1984–2002, 2002–2013, and 2013–2022 was performed, as well as CA-Markov hybrid model was employed to project the future LULC trends for 2030, 2040, and 2050. Four Landsat images acquired by different sensors were used as spatial–temporal data sources for the study region, including TM for 1984, ETM+ for 2002, and OLI for 2013 and 2022. Furthermore, a supervised classification technique was implemented in the image classification process. All remote sensing data was processed and modeled using IDRISI 7.02 software. Four main LULC categories were recognized in the study region: urban areas, cultivated lands, desert lands, and water bodies. The precision of LULC categorization analysis was high, with Kappa coefficients above 0.7 and overall accuracy above 87.5% for all classifications. The results obtained from estimating LULC change in the period from 1984 to 2022 indicated that built-up areas expanded to cover 12.5% of the study area in 2022 instead of 5.5% in 1984. This urban sprawl occurred at the cost of reducing old farmlands in old towns and villages and building new settlements on bare lands. Furthermore, cultivated lands increased from 45.5% of the total area in 1984 to 60.7% in 2022 due to ongoing soil reclamation projects in desert areas outside the Nile Valley. Moreover, between 1984 and 2022, desert lands lost around half of their area, while water bodies gained a very slight increase. According to the simulation and projection of the future LULC trends for 2030, 2040, and 2050, similar trends to historical LULC changes were detected. These trends are represented by decreasing desert lands and increasing urban and cultivated newly reclaimed areas. Concerning CA-Markov model validation, Kappa indices ranged across actual and simulated maps from 0.84 to 0.93, suggesting that this model was reasonably excellent at projecting future LULC trends. Therefore, using the CA-Markov hybrid model as a prediction and modeling approach for future LULC trends provides a good vision for monitoring and reducing the negative impacts of LULC changes, supporting land use policy-makers, and developing land management.

Quantifying the impact of climate and vegetation changes on runoff based on the budyko framework in the Lake Issyk-Kul Basin, Kyrgyzstan

Identifying and quantifying the drivers of runoff (R) variability is fundamental to our understanding of the hydrologic cycle and necessary for decision makers to manage water resources. Climate variables and vegetation are the main factors influencing the R. However, the effects of climate and vegetation changes on R are still poorly understood, especially in arid regions with limited water resources. This study quantifies the contribution of precipitation (PRE), potential evapotranspiration (ET0), and Normalized Difference Vegetation Index (NDVI) to R in Lake Issyk-Kul Basin (LIKB) dryland Central Asia by using the Budyko model. The results showed that R, PRE, and ET0 decreased from 2000 to 2020, while the NDVI and underlying parameter (ω) showed a slightly increasing trend. By using the Mann-Kendall (M-K) statistical approach, divided the R series into a baseline period (2000–2010) and a change period (2011–2020) based on the breakthrough point (2011). In the baseline period, R showed a decreasing trend, while in the change period, R showed an increasing trend of 1.8 mm/yr. The sensitivity analysis shows that a 1 mm increase in PRE results in a 0.48 mm increase in R (sensitivity coefficient to R is 0.48). Conversely, a 1 mm increase in ET0 (sensitivity coefficient to R of −0.03) and a 1 unit increase in NDVI (sensitivity coefficient to R of −343.31) lead to R decreasing by 0.03 and 343.31 mm, respectively. The relative contributions of PRE, ET0 and NDVI were 33.98%, −3.17% and 3.67%, respectively, suggesting that changes in PRE and NDVI contributed to the decrease in R while the opposite for ET0. PRE dominated the decrease in R, which decreased by 26.58 mm, leading to a decrease in R of 12.76 mm. A decrease of 65.33 mm in ET0 and an increase of 0.003 in NDVI resulted in an increase and decrease of 1.96 and 1.18 mm in R, respectively. This study enhances the understanding of the response of the water cycle to climate and vegetation changes in arid regions and can provide theoretical support for water resource management and ecological restoration.

Using remote sensing approach to analyze vegetation response to drought and landscape changes in arid regions

Arid regions, characterized by low annual precipitation, unique vegetation, and distinctive hydrological cycles, play a significant role in maintaining ecological balance. However, these regions, with their hostile and remote environments, present unique challenges for field research. This study utilizes remote sensing technology, particularly the Normalized Difference Vegetation Index (NDVI), to evaluate the ecosystem's response to drought and understand the relationship between vegetation variability and other landscape features including elevation, soil type, and changes in land use or land cover. Six sites within the city, each of 100 square kilometers and representing diverse landscapes, were selected for the study. Key datasets describing land features were collected from official and authentic websites. A series of ArcGIS-based data processing methods were applied to discern patterns in the relationship between landscape features and vegetation variability, with a particular focus on periods of wet and dry years. The wet and dry years are identified as 2005 and 2009 respectively, based on average rainfall data. Notably, NDVI values in the wet year are consistently higher than in the dry year, with the greatest differences observed in undeveloped or shrubland areas (sites 3, 5, and 6). In terms of land cover, urban development increases in sites 1, 2, and 4 between 2004 and 2008, while shrubland decreases in sites 3, 5, and 6. This development corresponds to a contraction of vegetation cover. The study areas are primarily characterized by loamy soils, with variations in clay and sand composition. These findings underscore the impacts of rainfall and urban development on vegetation health in arid regions.

Assessing the Vegetation Dynamics and Its Influencing Factors in Central Asia from 2001 to 2020

As vegetation plays a critical role in terrestrial ecosystems, understanding its status and variation is vital for preserving the stability of an ecosystem. Central Asia serves as a representative example of an arid and semi-arid region characterized by sparse vegetation and poor soils, making its vegetation particularly fragile and sensitive. To investigate the vegetation condition in the region, this study examined the spatial and temporal characteristics of vegetation variation from 2001 to 2020, utilizing the normalized difference vegetation index (NDVI) as an indicator. Meanwhile, trend analysis, Mann–Kendall abrupt change point test, geodetector, and correlation analysis were used to quantitatively analyze the natural and anthropogenic drivers of these variations over the past two decades. The results suggest that vegetation coverage in Central Asia was relatively low, with an annual average NDVI of 0.16 over the past 20 years. Moreover, the spatial distribution of NDVI in Central Asia exhibited significant spatial heterogeneity, with vegetation coverage declining from north to south and from east to west. Furthermore, the NDVI exhibited a slightly increasing trend during the period of 2001 to 2020 with an increased rate of 0.00025/yr. However, we detected an abrupt change point in vegetation dynamics in Central Asia around 2010, which indicated a significant shift in vegetation variation in the region. Land-use type has a great influence on the spatial heterogeneity of NDVI in Central Asia, which can explain 46% of the vegetation distribution in this region. Moisture factors such as precipitation and soil water content followed with 35% and 32% contributions, respectively. Regarding the temporal variation of NDVI, it is mainly driven by the fluctuation in precipitation, with the degree of influence of precipitation on NDVI varying for different regions in various geographical conditions. This study offers a more comprehensive insight into the spatial and temporal dynamics of NDVI in Central Asia and indicates that precipitation plays a significant role in driving the spatial heterogeneity and temporal variation of NDVI. These findings are essential for predicting vegetation changes in arid regions under future environmental conditions and formulating effective strategies to prevent and alleviate vegetation degradation.

Monitoring Lake Level Changes in Bosten Lake from 2003-2021 based on Multi-source Satellite Altimetry Data

The investigation of freshwater lake water resource changes in arid regions is crucial for understanding the evolution of regional lake dynamics under climate change, given their value and limited availability. This study aims to overcome the limitations of traditional water level monitoring methods and address the practical need for monitoring water level changes in Lake Bosten, located in the arid and semi-arid regions of China. To this end, we employed ICESat, ICESat-2, and CryoSat-2 altimetry data, as well as Landsat 8-OLI optical remote sensing data, to obtain the water level information for Bosten Lake from 2003 to 2022. We analyzed the temporal variation of the lake level and discussed the response of the lake level to climate and human activities in the context of temperature, precipitation data, and human water use. The water level of Lake Bosten exhibited a decreasing and then increasing trend, with three rising and three falling intervals, respectively. The intra-year variation of the lake water level exhibited bimodal characteristics, and the fluctuation range of water level during abundant water periods was greater than that during dry periods. Finally, we correlated the extracted lake water level with the measured water level at the hydrological station and found a significant positive linear correlation between the observed water level of Bosten Lake based on satellite altimetry and the measured water level at the hydrological station, with an overall correlation coefficient of 0.896 and 0.934 (P < 0.01), indicating high accuracy of the water level results obtained in this study. The dynamics of Lake Bosten are influenced by a combination of climate change, hydrological elements, and human activities, with a relatively weak relationship between climate change and lake volume and a strong influence of human activities. Multi-source altimetry satellites provide a powerful tool for monitoring lake water levels over a long time scale, which is critical for studying lake water level changes and their response to climate and the environment.

Geographical Detector-Based Research of Spatiotemporal Evolution and Driving Factors of Oasification and Desertification in Manas River Basin, China

Oasification and desertification are two essential processes of land use and cover (LULC) change in arid regions. Compared to desertification, which is widely regarded as the most severe global ecological issue, the importance of oasification has not received universal recognition. However, neglecting oasification can lead to detrimental outcomes to the effectiveness of ecological governance by affecting the comprehensiveness of environmental policies proposed only based on desertification. Therefore, this study incorporates oasification into the examination of desertification by analyzing land use data for five representative periods spanning from 1980 to 2020, as well as socioeconomic and environmental data from 2000 to 2010. The aim is to evaluate the spatial and temporal dynamics of oasification and desertification in the Manas River Basin and identify the underlying factors driving these processes. The findings indicated that (1) the general trend of oasification and desertification exhibited the expansion of oases and the retreat of deserts. Specifically, the oasification area showed a “decrease-increase-decrease” pattern over time, while the desertification area consistently decreased. (2) In terms of spatial distribution, oasification and desertification displayed a transition from scattered and disordered patterns to an overall more organized pattern, with the hotspot area of desertification shifting from Shawan County to Manas County over time. (3) Population density, average land GDP, soil type and annual precipitation significantly influenced the degree of oasification, with driving force q-values above 0.4, which were the key factors driving oasification. Population density and average land GDP significantly affected the degree of desertification, with driving force q-values above 0.35, which were the key factors driving desertification. The driving force of all factors increased significantly after the interaction, and socioeconomic factors influenced oasification and desertification more than other factors. The study’s findings aim to provide a scientific basis for land resource use, ecological governance and sustainable development in the Manas River basin.

Comparisons of climate change characteristics in typical arid regions of the Northern Hemisphere

In recent years, with the frequent occurrence of severe drought events, climate change in arid regions has become one of the research hotspots. However, previous studies mainly focused on a specific arid region, and the correlations and differences of drought among various arid regions have not been clearly understood. In this study, based on the latest monthly gridded dataset of the CRU, we compare the characteristics of climate change and its relationship with large-scale oceanic oscillation indexes in the three typical arid regions of Pan-Central-Asia (PCA), North America (NAm) and North Africa (NAf) in multiple perspectives. The results show that the precipitation in the PCA and NAm has increased obviously over the past 80 years, while the NAf precipitation has decreased. After the 1980s, the climate in the PCA and NAm show warm-wet types. This type of the former continues to the present, but the latter’s has changed to a warm-dry type since the 21st century. The NAf climate remains the warm-dry type since the 1990s. Nonetheless, the arid and semi-arid climate patterns in the three typical arid regions remain unchanged. The NAm precipitation has an anti-phase variability pattern compared with the NAf precipitation on both interdecadal and multi-decadal time scales. The Pacific Decadal Oscillation (PDO) has a great influence on the precipitation of the PCA and NAm. The temperature of three arid regions is significantly related to the variations in the Arctic oscillation (AO). In the inland arid region, the contribution of strong warming effect during cold season to the whole year is much greater than that during warm season, while the contribution of the coastal arid regions in warm season is greater. The precipitation in the mid-latitude arid regions is dominated by cold-season precipitation regardless of whether these regions are near the sea or not. The precipitation in the low-latitude arid regions has little difference between cold and warm seasons.

Attribution of NDVI Dynamics over the Globe from 1982 to 2015

Satellite remote sensing has witnessed a global widespread vegetation greening since the 1980s. However, reliable observation-based quantitative knowledge on global greening remains obscure due to uncertainties in model simulations and the contribution of natural variability is largely unknown. Here, we revisit the attribution of global vegetation changes using the Time Series Segment and Residual Trend (TSS-RESTREND) method. Results showed global vegetation significantly greening over 40.6% of the vegetated grids, whereas vegetation significantly browning over 11.6% of the vegetated grids. The attribution results based on the TSS-RESTREND method show that CO2 fertilization (CO2) plays an influential role in vegetation changes over 61.4% of the global vegetated areas, followed by land use (LU, 23.5%), climate change (CC, 7.3%), and climate variability (CV, 1.5%). The vegetation greening can be largely attributed to CO2 fertilization while the vegetation browning is mainly caused by LU. Meanwhile, we also identify positive impacts of LU and CC on vegetation change in arid regions but negative impacts in humid regions. Our findings indicate spatial heterogeneity in causes behind global vegetation changes, providing more detailed references for global vegetation modeling.

Relationships Between Soil Microbial Diversities Across an Aridity Gradient in Temperate Grasslands : Soil Microbial Diversity Relationships.

Soil microbes assemble in highly complex and diverse microbial communities, and microbial diversity patterns and their drivers have been studied extensively. However, diversity correlations and co-occurrence patternsbetween bacterial, fungal, and archaeal domains and between microbial functional groups in arid regions remain poorly understood. Here we assessed the relationships between the diversity and abundance of bacteria, fungi, and archaea and explored how environmental factors influence these relationships. We sampled soil along a 1500-km-long aridity gradient in temperate grasslands of Inner Mongolia (China) and sequenced the 16S rRNA gene of bacteria and archaea and the ITS2 gene of fungi. The diversity correlations and co-occurrence patterns between bacterial, fungal, and archaeal domains and between different microbial functional groups were evaluated using α-diversity and co-occurrence networks based on microbial abundance. Our results indicate insignificant correlations among the diversity patterns of bacterial, fungal, and archaeal domains using α-diversity but mostly positive correlations among diversity patterns of microbial functional groups based on α-diversity and co-occurrence networks along the aridity gradient. These results suggest that studying microbial diversity patterns from the perspective of functional groups and co-occurrence networks can provide additional insights on patterns that cannot be accessed using only overall microbial α-diversity. Increase in aridity weakens the diversity correlations between bacteria and fungi and between bacterial and archaeal functional groups, but strengthens the positive diversity correlations between bacterial functional groups and between fungal functional groups and the negative diversity correlations between bacterial and fungal functional groups. These variations of the diversity correlations are associated with the different responses of microbes to environmental factors, especially aridity. Our findings demonstrate the complex responses of microbial community structure to environmental conditions (especially aridity) and suggest that understanding diversity correlations and co-occurrence patternsbetween soil microbial groups is essential for predicting changes in microbial communities under future climate change in arid regions.

Simulating winter wheat production potential under near-future climate change in arid regions of northeast Iran

Simulating winter wheat production potential under near-future climate change in arid regions of northeast Iran

Treated wastewater for meeting challenges of climate change in arid regions

This paper focuses on treated wastewater in addressing the impacts of climate change on water sources, especially in arid and semi-arid regions that suffer anyway from water scarcity. Jordan with its severe water situation is expected to be defenseless to climate change effects taking into account the unbalance between available water resources and urgent demands. The paper helps in assessing treated wastewater as an effective adaptation measure to cope with water scarcity in Jordan and to consider it as an integral part of the national water budget. Wastewater is a promising alternative to cover the shortage in water supply in regions where severe drought is an anticipated effect of climate change, also to provide water of good quality to secure drinking water for the population, at the same time to increase the efficiency of the uses of the available water sources. Jordan has 36 wastewater treatment plants in operation scattered throughout the country and producing about 430,000 CM/day of reclaimed wastewater. According to the reports Ministry of Water and Irrigation, more than 85 percent of this water is of in full compliance to the quality required for irrigation of field crops and forest trees according to JS 893/2006.

Correction to: Simulation and Estimation of Future Precipitation Changes in Arid Regions: a Case Study of Xinjiang, Northwest China

Correction to: Simulation and Estimation of Future Precipitation Changes in Arid Regions: a Case Study of Xinjiang, Northwest China

Simulation and estimation of future precipitation changes in arid regions: a case study of Xinjiang, Northwest China

Precipitation is critical for maintaining the stability of an ecosystem, especially in arid regions. This study primarily focuses on climatic changes during present (from 1985 to 2005) and future (from 2040 to 2059) periods in Xinjiang, Northwest China. In this study, the Weather Research and Forecasting model is implemented in Xinjiang to efficiently predict the future climate. Moreover, the National Climate Research Center Community Climate System Model version 4 is employed for the mid-21st century under representative concentration pathways 4.5 and 8.5 (RCP4.5 and RCP8.5, respectively). Our results indicate that the amount of annual precipitation will increase in the future under RCP4.5 and RCP8.5 in Xinjiang, especially in mountainous areas. The increase in precipitation is predicted to be much smaller under RCP8.5 than under RCP4.5, except in Southern Xinjiang. Moreover, the increasing precipitation predicted in Xinjiang implies that the current humid and warm conditions will persist, thereby further indicating that Xinjiang is still currently suffering from a dry climate. The largest increase in seasonal precipitation is predicted to occur in spring and summer in Tianshan and Northern Xinjiang, whereas this phenomenon is predicted to occur in spring and winter in Southern Xinjiang. In addition, it is predicted that daily heavy precipitation events will occur more frequently in various subregions of Xinjiang, although light rain events will remain dominant. Finally, the relative humidity is closely related to the changes in annual and seasonal precipitation.